How do I get the inverse of a matrix in python? I’ve implemented it myself, but it’s pure python, and I suspect there are faster modules out there to do it.

You should have a look at numpy if you do matrix manipulation. This is a module mainly written in C, which will be much faster than programming in pure python. Here is an example of how to invert a matrix, and do other matrix manipulation.

```
from numpy import matrix
from numpy import linalg
A = matrix( [[1,2,3],[11,12,13],[21,22,23]]) # Creates a matrix.
x = matrix( [[1],[2],[3]] ) # Creates a matrix (like a column vector).
y = matrix( [[1,2,3]] ) # Creates a matrix (like a row vector).
print A.T # Transpose of A.
print A*x # Matrix multiplication of A and x.
print A.I # Inverse of A.
print linalg.solve(A, x) # Solve the linear equation system.
```

You can also have a look at the array module, which is a much more efficient implementation of lists when you have to deal with only one data type.

Make sure you really need to invert the matrix. This is often unnecessary and can be numerically unstable. When most people ask how to invert a matrix, they really want to know how to solve Ax = b where A is a matrix and x and b are vectors. It’s more efficient and more accurate to use code that solves the equation Ax = b for x directly than to calculate A inverse then multiply the inverse by B. Even if you need to solve Ax = b for many b values, it’s not a good idea to invert A. If you have to solve the system for multiple b values, save the Cholesky factorization of A, but don’t invert it.

It is a pity that the chosen matrix, repeated here again, is either singular or badly conditioned:

```
A = matrix( [[1,2,3],[11,12,13],[21,22,23]])
```

By definition, the inverse of A when multiplied by the matrix A itself must give a unit matrix. The A chosen in the much praised explanation does not do that. In fact just looking at the inverse gives a clue that the inversion did not work correctly. Look at the magnitude of the individual terms – they are very, very big compared with the terms of the original A matrix…

It is remarkable that the humans when picking an example of a matrix so often manage to pick a singular matrix!

I did have a problem with the solution, so looked into it further. On the ubuntu-kubuntu platform, the debian package numpy does not have the matrix and the linalg sub-packages, so in addition to import of numpy, scipy needs to be imported also.

If the diagonal terms of A are multiplied by a large enough factor, say 2, the matrix will most likely cease to be singular or near singular. So

```
A = matrix( [[2,2,3],[11,24,13],[21,22,46]])
```

becomes neither singular nor nearly singular and the example gives meaningful results… When dealing with floating numbers one must be watchful for the effects of inavoidable round off errors.

Thanks for your contribution,

OldAl.

You could calculate the determinant of the matrix which is recursive

and then form the adjoined matrix

I think this only works for square matrices

Another way of computing these involves gram-schmidt orthogonalization and then transposing the matrix, the transpose of an orthogonalized matrix is its inverse!

Numpy will be suitable for most people, but you can also do matrices in Sympy

Try running these commands at http://live.sympy.org/

```
M = Matrix([[1, 3], [-2, 3]])
M
M**-1
```

For fun, try `M**(1/2)`

If you hate numpy, get out RPy and your local copy of R, and use it instead.

(I would also echo to make you you really need to invert the matrix. In R, for example, linalg.solve and the solve() function don’t actually do a full inversion, since it is unnecessary.)

Tags: python